Real-Time Automated Interpretation of Clinical Narratives

Abstract

Techniques for enabling real-time automated interpretation of clinical narratives are disclosed. The automated interpretation can be achieved by translating narrative text into a clinical terminology-encoded structural representation such as the Systemized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) example of such a clinical terminology. The translation process enables the generation of both pre-coordinated and post-coordinated SNOMED CT concept expressions.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application claims priority to U.S. Provisional Application No. 61 / 467,603, filed Mar. 25, 2011, which is incorporated herein by reference for all purposes.BACKGROUND[0002]Embodiments of this invention relate in general to natural language processing, and in particular to techniques for interpreting clinical narratives.[0003]Clinicians delivering healthcare typically document progress, findings, plans, and decisions in the form of textual notes or reports (i.e., clinical narratives) in a patient record of some kind The language used to create these clinical narratives is rich, complex, and specialized. Clinical narratives are often described as semi-structured—neither random nor easily predictable. Very subtle changes in the word content of a clinical narrative can have a dramatic effect on meaning; for example, “evidence of malignancy was found” versus “no evidence of malignancy was found.”[0004]The linguistic subtleties and complexi...

AI Technical Summary

Problems solved by technology

Clinicians delivering healthcare typically document progress, findings, plans, and decisions in the form of textual notes or reports (i.e., clinical narratives) in a patient record of some kind The language used to create these clinical narratives is rich, complex, and specialized.

The linguistic subtleties and complexity of clinical language make it difficult to meaningfully interpret clinical narratives in an automated manner.

Current EHR systems either (1) disallow entry of freeform narratives and require users to enter clinical information using a rigid, predetermined set of data entry fields, or (2) allow entry of freeform narratives but do not perform any processing or interpretation of the text.

With approach (1), the rigid structure imposed on users at the time of data entry results in low compliance and lost information.

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